README.md

---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/structured_data_with_cot_dataset_512_v2
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- qlora
- lora
- structured-output
---

# qwen3-4b-structeval-lora

This repository provides a LoRA adapter fine-tuned from **Qwen/Qwen3-4B-Instruct-2507**
using **QLoRA (4-bit, Unsloth)**.

> **Note**  
> This repository contains **LoRA adapter weights only**.  
> The base model must be loaded separately.

## Training Objective
This adapter is trained to improve structured output accuracy  
(JSON / YAML / XML / TOML / CSV).

Loss is applied **only to the final assistant output**, while intermediate
reasoning (Chain-of-Thought) is masked.

## Training Configuration
- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: QLoRA (4-bit)
- Max sequence length: 512
- Epochs: 2
- Learning rate: 1e-05
- LoRA: r=64, alpha=128

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "igaritak/qwen3-4b-structeval-lora"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)
```

## Sources & License (IMPORTANT)

### Training data
- Dataset: `u-10bei/structured_data_with_cot_dataset_512_v2`
- License: **MIT License**

### Base model
- Model: `Qwen/Qwen3-4B-Instruct-2507`
- License: **Apache-2.0**

● Compliance: Users must comply with both the dataset's attribution requirements and the base model's original terms of use.